The paper describes a novel approach to automated accent identification by training a speech recogniser to distinguish between different versions of the phonemes that make up the language. In this approach, a standard speech recogniser is trained with data where the critical phonemes (vowels and a small set of consonants) are marked as coming in several varieties, one per accent. This is followed by inspection of the output of the recogniser to determine which versions predominate in a given utterance. Put simply, if a speaker produces phonemes that match Levantine versions of those phonemes they are characterised as speaking with a Levantine accent. Similarly, if they produce phonemes that match the Egyptian versions they should be characterised as speaking with an Egyptian accent, and so on. The accuracy of this approach to classifying speakers’ accents varies from 79 to 86% when tested on speakers from the five main Arabic accent groups (Gulf, Iraqi, Egyptian, Levantine, Maghrebi), depending on a range of conditions discussed in the paper. These results are an improvement on the state of the art for accent recognition for Arabic, i.e. for classifying spoken, rather than written, material on the basis of the speaker’s geographical origin.

Existing research on coping accentuates the role of positive emotions as defensive mechanisms to cope with stressful situations and the ensuing negative emotions. The same literature justifies the long-term effects of positive emotions that help build lasting resilience. Grounded in theories of coping and resilience, this paper (1) identifies the emotions that people actuate to cope with adversaries and (2) evaluates the resulting long-lasting adaptation and resilience. To do this, we examined the emotions felt by Qatar residents due to a land, sea, and air blockade enforced by neighbouring counties. Accordingly, we analysed 160,000 Arabic tweets originating from Qatar between June-2017 and March-2018 using a novel machine-learning algorithm termed Weighted Conditional Probability. Our algorithm achieved state-of-the-art performance when compared with the often-used Support Vector Machine, Naive Bayes and Deep Neural Nets algorithms. Results show that, while Qatar residents experienced an emotional roller coaster during the blockade, they used positive emotions like love and optimism to cope with adversities and accompanying emotions of fear and anger. Moreover, our analysis reveals that their adaptive resilient capacities gradually strengthened during the nine months of blockade. The study supports the renowned theory of positive emotions using an advanced methodology and a large-scale dataset.

It is well-known that the Arabic language poses non-trivial issues for Automatic Speech Recognition (ASR) systems. This paper is concerned with the problems posed by the complex morphology of the language and the absence of diacritics in the written form of the language. Several acoustic and language models are built using different transcription resources, namely a grapheme-based transcription which uses non-diacriticised text materials, phoneme-based transcriptions obtained from automatic diacritisation tools (SAMA or MADAMIRA), and a predefined dictionary. The paper presents a comprehensive assessment for the aforementioned transcription schemes by employing them in building a collection of Arabic ASR systems using the GALE (phase 3) Arabic broadcast news and broadcast conversational speech datasets LDC (2015), which include 260 h of recorded material. Contrary to our expectations, the experimental evidence confirms that the use of grapheme-based transcription is superior to the use of phoneme-based transcription. To investigate this further, several modifications are applied to the MADAMIRA analysis by applying a number of simple phonological rules. These improvements have a substantial effect on the systems’ performance, but it is still inferior to the use of a simple grapheme-based transcription. The research also examined the use of a manually diacriticised subset of the data in training the ASR system and compared it with the use of grapheme-based transcription and phoneme-based transcription obtained from MADAMIRA. The goal of this step is to validate MADAMIRA’s analysis. The results show that using the manually diacriticised text in generating the phonetic transcription can significantly decrease the WER compared to the use of MADAMIRA diacriticised text and also the isolated graphemes. The results obtained strongly indicate that providing the training model with less information about the data (only graphemes) is less damaging than providing it with inaccurate information.

Research in Arabic automatic speech recognition (ASR) is constrained by datasets of limited size, and of highly variable content and quality. Arabic-language resources vary in the attributes that affect language resources in other languages (noise, channel, speaker, genre), but also vary significantly in the dialect and level of formality of the spoken Arabic they capture. Many languages suffer similar levels of cross-dialect and cross-register acoustic variability, but these effects have been under-studied. This paper is an experimental analysis of the interaction between classical ASR corpus-compensation methods (feature selection, data selection, gender-dependent acoustic models) and the dialect-dependent/register-dependent variation among Arabic ASR corpora. The first interaction studied in this paper is that between acoustic recording quality and discrete pronunciation variation. Discrete pronunciation variation can be compensated by using grapheme-based instead of phone-based acoustic models, and by filtering out speakers with insufficient training data; the latter technique also helps to compensate for poor recording quality, which is further compensated by eliminating delta-delta acoustic features. All three techniques, together, reduce Word Error Rate (WER) by between 3.24% and 5.35%. The second aspect of dialect and register variation to be considered is variation in the fine-grained acoustic pronunciations of each phoneme in the language. Experimental results prove that gender and dialect are the principal components of variation in speech, therefore, building gender and dialect-specific models leads to substantial decreases in WER. In order to further explore the degree of acoustic differences between phone models required for each of the dialects of Arabic, cross-dialect experiments are conducted to measure how far apart Arabic dialects are acoustically in order to make a better decision about the minimal number of recognition systems needed to cover all dialectal Arabic. Finally, the research addresses an important question: how much training data is needed for building efficient speaker-independent ASR systems? This includes developing some learning curves to find out how large must the training set be to achieve acceptable performance.

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

A wide range of algorithms for computing textual similarity have been proposed. Much recent work has been aimed at calculating lexical similarity, but in general such calculations have to be treated as components in larger algorithms for computing similarity between sentences. In the current paper we describe a refinement of the well-known dynamic-time warping (DTW) algorithm for calculating the string edit distance between a pair of texts. The refined version of this algorithm allows for a range of constrained permutations without increasing the complexity of the underlying algorithm.

In this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets. We attempted “Affect in Tweets”, specifically Task E-c: Detecting Emotions (multi-label classification). Our method is based on preprocessing the tweets and creating word vectors combined with a self correction step to remove noise. We also make use of emotion specific thresholds. The final submission was selected upon the best performance achieved, selected when using a range of thresholds. Our system was evaluated on the Arabic and English datasets provided for the task by the competition organisers, where it ranked 2nd for the Arabic dataset (out of 14 entries) and 12th for the English dataset (out of 35 entries).

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. As such, it seems likely that standard machine learning algorithms such as deep neural networks (DNNs) will provide an effective approach. We describe an alternative approach, involving the construction of a weighted lexicon of sentiment terms, which significantly outperforms the use of DNNs. The moral of the story is that DNNs are not a universal panacea, and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

To facilitate cross-lingual studies, there is an increasing interest in identifying linguistic universals. Recently, a new universal scheme was designed as a part of universal dependency project. In this paper, we map the Arabic tweets dependency treebank (ATDT) to the Universal Dependency (UD) scheme to compare it to other language resources and for the purpose of cross-lingual studies.

This paper presents an approach to generating common sense knowledge written in raw English sentences.Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries.Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side ⇒ right-hand side.In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions.A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are 'True' or not.For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.

This paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The theorem prover is customized to accept dependency trees and apply inference rules to these trees. The inference rules are automatically generated as syllogistic rules from our test data (FraCaS test suite). The theorem prover exploits a non-deterministic matching algorithm within a standard backward chaining inference engine. We employ continuation programming as a way of seamlessly handling the combination of these two non-deterministic algorithms. Testing the matching algorithm on “Generalized quantifiers” and “adjectives” topics in FraCaS (MacCartney and Manning 2007), we achieved an accuracy of 92.8% of the single-premise cases. For the multi-steps of inference, we checked the validity of our syllogistic rules and then extracted four generic instances that can be applied to more than one problem.

This paper aims at determining the best way to exploit the phonological properties of the Arabic language in order to improve the performance of the speech recognition system. One of the main challenges facing the processing of Arabic is the effect of the local context, which induces changes in the phonetic representation of a given text, thereby causing the recognition engine to misclassify it. The proposed solution is to develop a set of language-dependent grapheme-to-allophone rules that can predict such allophonic variations and hence provide a phonetic transcription that is sensitive to the local context for the automatic speech recognition system. The novel aspect of this method is that the pronunciation of each word is extracted directly from a context-sensitive phonetic transcription rather than a predefined dictionary that typically does not reflect the actual pronunciation of the word. The paper also aims at employing the stress feature as one of the supra-segmental characteristics of speech to enhance the acoustic modelling. The effectiveness of applying the proposed rules has been tested by comparing the performance of a dictionary based system against one using the automatically generated phonetic transcription. The research reported an average of 9.3% improvement in the system’s performance by eliminating the fixed dictionary and using the generated phonetic transcription to learn the phone probabilities. Marking the stressed vowels with separate stress markers leads to a further improvement of 1.7%.

In this paper, we propose using a “bootstrapping” method for constructing a dependency treebank of Arabic tweets. This method uses a rule-based parser to create a small treebank of one thousand Arabic tweets and a data-driven parser to create a larger treebank by using the small treebank as a seed training set. We are able to create a dependency treebank from unlabelled tweets without any manual intervention. Experiments results show that this method can improve the speed of training the parser and the accuracy of the resulting parsers.

Sentiment analysis can help analyse trending topics such as political crises and predict it before it occurs. Yet, analysing sentiments in Arabic texts has not been explored much in the extant literature. In this paper, we present a new tool that applies sentiment analysis to Arabic text tweets using a combination of parameters. Those parameters are (1) the time of the tweets, (2) preprocessing methods like stemming and retweets, (3) n-grams features, (4) lexicon-based methods, and (5) machine-learning methods. Users can select a topic and set their desired parameters. The model detects the polarity (negative, positive, both, and neutral) of the topic from the recent related tweets and display the results. The tool is trained with 8000 randomly selected and evenly-labelled Arabic tweets. Our experiments show that the Naive Bayes machine-learning approach is the most accurate in predicting topic polarity. The tool is useful for intermediate and expert users and can help guide them in choosing the best combinations of parameters for sentiment analysis.

Part-of-Speech (POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because they are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengths. Arabic tweets also show a further range of linguistic phenomena such as usage of different dialects, romanised Arabic and borrowing foreign words. In this paper, we present an evaluation and a detailed error analysis of state-of-the-art POS taggers for Arabic when applied to Arabic tweets. On the basis of this analysis, we combine normalisation and external knowledge to handle the domain noisiness and exploit bootstrapping to construct extra training data in order to improve POS tagging for Arabic tweets. Our results show significant improvements over the performance of a number of well-known taggers for Arabic.

Ambiguities in natural languages make processing (parsing) them a difficult task. Parsing is even more difficult when dealing with a structurally complex natural language such as Arabic. In this paper, we briefly highlight some of the complex structure of Arabic, and we identify different parsing approaches and briefly discuss their limitations. Our goal is to produce a hybrid parser, by combining different parsing approaches, which retains the advantages of data-driven approaches but is guided by a set of grammatical rules to produce more accurate results. We describe a novel technique for directly combining different parsing approaches. Results for our initial experiments that we have conducted in this work, and our plans for future work are also presented.

In a world where news is being generated almost continuously by many different news providers on many different platforms, it would be useful in certain industries to be able to determine how much of that news is actually being read, which news items are not interest generating or, indeed, if there are topics being discussed on Twitter that have not even been reported in the news. Twitter generates vast numbers of Tweets daily and has a massive active user base, so it is ideal as a way of gauging what news people are, or are not, interested in. This paper proposes a technique to efficiently relate Tweets to news articles and then to determine which news articles are of interest, which are not, and what is being discussed on Twitter that is not even in the news.

Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substantial differences between them in lexicon and syntax. In this paper, we introduce a light Arabic stemmer for Arabic tweets. Our results show improvements over the performance of a number of well-known stemmers for Arabic.